The New Speed: How AI Projects Go Live in Weeks Instead of Months

Why Time is the Decisive Factor in AI Projects

Anyone who needs 6 months for an AI project today has already lost — not because of the competition, but because of the learning curve.

In the last 18 months, I've built more working products than in the 10 years before: PostFlow, eatsnaplaf.com, History Simulator, Elternhub Tools, Corporate Chatbots.

The biggest insight: Speed = Learning Cycles × Radical Simplification × Pragmatic Architecture.

While other teams are still discussing use cases, we already have working prototypes. While others wait for the perfect architecture, we learn from real user data. That's the difference between success and stagnation.

Why AI Projects Are Traditionally So Slow (And Why It's All Unnecessary)

Concrete obstacles I see again and again in corporates:

• Endless "Use Case Scoping" — 6 weeks of discussion, 0 lines of code
• 40-page PowerPoints — that no one reads, but everyone approves
• 5 Stakeholders who "just want to give quick feedback" — and then make you wait 3 weeks
• Technically complex setups — RAG, vector search, data teams, before it's even clear if the problem exists
• Unrealistic security concerns — "We need the AI governance guidelines first" (which don't exist yet)

We start instead with:

• A simple Python app
• Hetzner server (€5/month)
• Coolify deployment (free, open source)
• Postgres + MinIO (if needed)
• Frontend SSR in Jinja2 (no React overhead)

80% of AI projects fail not because of technology, but because of over-engineering before the first test.

Most teams spend more time planning than learning. That's the cardinal sin.

The Reruption Method: 3-Week AI Projects

Week 1 – Understand, Simplify, Prototype

In the first week, it's about speed, not perfection:

• 1 Workshop, no 100-page scope — we talk directly with users
• Direct translation of requirements into a mini functional spec (1 page, not 50)
• First working UI in Python/Jinja2 — no mockups, real feedback
• LLM "Brain" quickly created through domain capture instead of RAG — more reliable, faster, cheaper
• Model choice: always best available (GPT-4, Claude, depending on requirements)

Example from my practice:

• PostFlow Scheduling V1 → was functional in 3 days
• Custom B2B chatbot for real estate companies → adapted in 5 days, domain knowledge hyper-robust

The difference: We don't ask "What could happen?", but "What do we need to know now to start?"

Week 2 – Hardening, Use Case Expansion, First Real Data

Now it gets serious — but still fast:

• User authentication — Auth0 or simple sessions
• Limits & Stripe Credits — monetization from day 1
• Logging & Monitoring — know what's happening
• Prompt optimizations & decision logic — learn from real data
• First automations — webhooks, ingestion, batch jobs

My experience:

• At PostFlow: AI analysis of LinkedIn profiles → multiple iterations, but all within 10 days
• At History Simulator: daily content mass production → automatable within a week

In week 2, we see the first real users. That's the moment when we learn, not speculate.

Week 3 – Onboarding + Rollout + Feedback Loops

The last week is for reality:

• First real user feedback — not from interviews, but from behavior
• Small analytics — Umami + Hotjar, no enterprise solution
• Fix bugs — quickly, pragmatically
• Improve performance — where it really hurts
• Handover to the team or operations for ourselves

Our pattern: "We'd rather ship 100 small iterations than one perfect big bang."

After 3 weeks, we have a working product, real users, and real data. That's more than most teams have after 6 months.

The Technical Principles That Make Us Fast

A) Simple Tech Stacks Beat Everything

Complexity is the enemy of speed:

• Python SSR (Jinja2) → fast, readable, no React hell
• One repo, one deployment → no microservice architecture for 3 users
• Coolify auto-build + preview environments → deployment in minutes
• Hetzner → no vendor lock-in, full control, €5 instead of €500

We don't use the latest technology — we use the simplest that works.

B) No RAG Usage — Domain Capture Instead of Vector Search

This is our USP:

• RAG is slow — vector search takes time
• RAG is unstable — hallucinations from wrong documents
• RAG is expensive — embeddings, vector databases, infrastructure

Domain Capture delivers more reliable answers:

• We encode knowledge directly into prompts
• Fewer moving parts → fewer errors
• Ship faster → learn more

For most use cases, you don't need vector search — you need good prompt engineering.

C) Programmatic Everything

Scaling through code, not manpower:

• Reusable templates — built once, used a thousand times
• High-speed generation for SEO — 1,000 pages/day is no problem
• Databases + CSV/JSON inputs → instantly scalable

If something needs to be done twice, it gets automated.

D) AI-IDE Workflow

We use AI not only in the product, but also in development:

• Cursor as "second developer" — code generation at the push of a button
• LLM-guided coding → 3–5× faster than traditional
• Automatic generation of 1,000 pages/day — no copy-paste
• Improved diff explanation → faster to fix

AI doesn't just accelerate our products — it accelerates our development too.

Real-World Examples from Our Projects (Concrete Timeframes)

PostFlow

• First working version: 10 days
• GPT scheduling system: 72h
• LinkedIn analysis module: weekend project
• Multi-language prompt engine: 1 day

PostFlow shows how fast you can build a complete B2B product when you skip perfection and focus on learning.

Eatsnaplaf (Photo Food Analyzer)

• MVP: 4 days
• TikTok test videos: next week
• AI food classifier: <48h

From idea to working product in less than a week. That's the new speed.

History Simulator

• Generative content stack: 1 week
• 3D scene prompts & templates: 3 days
• 1,000+ page index already in the first week

Scaling through automation, not manpower.

Corporate Chatbots

• B2B real estate: 5 days
• Healthcare infobot: 8 days
• Recruiting bot: 6 days

Every chatbot is different — but the method stays the same: start fast, learn fast, iterate fast.

The Cultural Factor: Co-Preneurship

We work as if it were our product. No classic consultants. Bold, pragmatic, entrepreneurial energy. Decisions in hours, not in steering committees.

That's the difference between consulting and co-preneurship:

• Consultants deliver PowerPoints
• Co-preneurs deliver working products

We have skin in the game. If the project fails, we fail with it. That makes the difference.

Why the Fast Method Produces Better Results

Three reasons why speed is better:

1. More learning cycles → better products: Without early users, LinkedIn scheduling at PostFlow would never have matured so quickly. Users first, then features, never the other way around.
2. Better alignment between business + tech: When tech and business see the same thing in the same week, better solutions emerge. No translation errors between departments.
3. Clearer prioritization: Focus on what brings reach, revenue, or efficiency. Everything else gets cut.

Slow projects collect features. Fast projects collect users.

Conclusion: AI Projects Don't Fail Because of AI — They Fail Because of Slowness

The teams that win in 2025 aren't those with the best ideas, but those that survive the most iterations.

Speed isn't a nice-to-have — it's the decisive competitive advantage. Anyone who still needs 6 months for an AI project today won't be playing tomorrow.

The question isn't "Can we do this perfectly?" The question is "Can we test this next week?"

If the answer is "Yes," you've won.